生体模倣の確率的リカレントネットワークによる効率的なオンライン適応
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks
認知的不協和に着想を得た学習信号とメンタルリプレイを用い、確率的リカレントネットワークが少数の物理相互作用から未知環境に数秒で適応する枠組みを提案し、KUKA LWRアームで検証した。
著者: Daniel Tanneberg, Jan Peters, Elmar Rueckert
分類: cs.AI, cs.LG, cs.RO, stat.ML
原文アブストラクト
Autonomous robots need to interact with unknown, unstructured and changing environments, constantly facing novel challenges. Therefore, continuous online adaptation for lifelong-learning and the need of sample-efficient mechanisms to adapt to changes in the environment, the constraints, the tasks, or the robot itself are crucial. In this work, we propose a novel framework for probabilistic online motion planning with online adaptation based on a bio-inspired stochastic recurrent neural network. By using learning signals which mimic the intrinsic motivation signalcognitive dissonance in addition with a mental replay strategy to intensify experiences, the stochastic recurrent network can learn from few physical interactions and adapts to novel environments in seconds. We evaluate our online planning and adaptation framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is shown by learning unknown workspace constraints sample-efficiently from few physical interactions while following given way points.
